Overview

Dataset statistics

Number of variables28
Number of observations115609
Missing cells0
Missing cells (%)0.0%
Duplicate rows269
Duplicate rows (%)0.2%
Total size in memory24.7 MiB
Average record size in memory224.0 B

Variable types

Text9
Categorical4
Numeric14
DateTime1

Alerts

Dataset has 269 (0.2%) duplicate rowsDuplicates
payment_value is highly overall correlated with priceHigh correlation
customer_zip_code_prefix is highly overall correlated with customer_stateHigh correlation
price is highly overall correlated with payment_value and 1 other fieldsHigh correlation
product_weight_g is highly overall correlated with price and 3 other fieldsHigh correlation
product_length_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
product_height_cm is highly overall correlated with product_weight_gHigh correlation
product_width_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
customer_state is highly overall correlated with customer_zip_code_prefixHigh correlation
order_status is highly imbalanced (93.4%)Imbalance

Reproduction

Analysis started2024-04-25 02:24:35.804559
Analysis finished2024-04-25 02:24:53.716036
Duration17.91 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

Distinct96516
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:53.813431image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83927 ?
Unique (%)72.6%

Sample

1st rowe481f51cbdc54678b7cc49136f2d6af7
2nd rowe481f51cbdc54678b7cc49136f2d6af7
3rd rowe481f51cbdc54678b7cc49136f2d6af7
4th row128e10d95713541c87cd1a2e48201934
5th row0e7e841ddf8f8f2de2bad69267ecfbcf
ValueCountFrequency (%)
895ab968e7bb0d5659d16cd74cd1650c 63
 
0.1%
fedcd9f7ccdc8cba3a18defedd1a5547 38
 
< 0.1%
fa65dad1b0e818e3ccc5cb0e39231352 29
 
< 0.1%
ccf804e764ed5650cd8759557269dc13 26
 
< 0.1%
68986e4324f6a21481df4e6e89abcf01 24
 
< 0.1%
c6492b842ac190db807c15aff21a7dd6 24
 
< 0.1%
465c2e1bee4561cb39e0db8c5993aafc 24
 
< 0.1%
a3725dfe487d359b5be08cac48b64ec5 24
 
< 0.1%
6d58638e32674bebee793a47ac4cbadc 24
 
< 0.1%
285c2e15bebd4ac83635ccc563dc71f4 22
 
< 0.1%
Other values (96506) 115311
99.7%
2024-04-25T07:54:53.999833image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 232347
 
6.3%
b 232260
 
6.3%
6 232183
 
6.3%
e 231974
 
6.3%
c 231513
 
6.3%
3 231489
 
6.3%
1 231448
 
6.3%
7 231435
 
6.3%
8 231366
 
6.3%
a 231122
 
6.2%
Other values (6) 1382351
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2311582
62.5%
Lowercase Letter 1387906
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 232347
10.1%
6 232183
10.0%
3 231489
10.0%
1 231448
10.0%
7 231435
10.0%
8 231366
10.0%
2 230768
10.0%
9 230559
10.0%
0 230074
10.0%
5 229913
9.9%
Lowercase Letter
ValueCountFrequency (%)
b 232260
16.7%
e 231974
16.7%
c 231513
16.7%
a 231122
16.7%
f 230865
16.6%
d 230172
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2311582
62.5%
Latin 1387906
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
4 232347
10.1%
6 232183
10.0%
3 231489
10.0%
1 231448
10.0%
7 231435
10.0%
8 231366
10.0%
2 230768
10.0%
9 230559
10.0%
0 230074
10.0%
5 229913
9.9%
Latin
ValueCountFrequency (%)
b 232260
16.7%
e 231974
16.7%
c 231513
16.7%
a 231122
16.7%
f 230865
16.6%
d 230172
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 232347
 
6.3%
b 232260
 
6.3%
6 232183
 
6.3%
e 231974
 
6.3%
c 231513
 
6.3%
3 231489
 
6.3%
1 231448
 
6.3%
7 231435
 
6.3%
8 231366
 
6.3%
a 231122
 
6.2%
Other values (6) 1382351
37.4%
Distinct96516
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:54.143146image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83927 ?
Unique (%)72.6%

Sample

1st row9ef432eb6251297304e76186b10a928d
2nd row9ef432eb6251297304e76186b10a928d
3rd row9ef432eb6251297304e76186b10a928d
4th rowa20e8105f23924cd00833fd87daa0831
5th row26c7ac168e1433912a51b924fbd34d34
ValueCountFrequency (%)
270c23a11d024a44c896d1894b261a83 63
 
0.1%
13aa59158da63ba0e93ec6ac2c07aacb 38
 
< 0.1%
9af2372a1e49340278e7c1ef8d749f34 29
 
< 0.1%
92cd3ec6e2d643d4ebd0e3d6238f69e2 26
 
< 0.1%
86cc80fef09f7f39df4b0dbce48e81cb 24
 
< 0.1%
6ee2f17e3b6c33d6a9557f280edd2925 24
 
< 0.1%
63b964e79dee32a3587651701a2b8dbf 24
 
< 0.1%
d22f25a9fadfb1abbc2e29395b1239f4 24
 
< 0.1%
2ba91e12e5e4c9f56b82b86d9031d329 24
 
< 0.1%
b246eeed30b362c09d867b9e598bee51 22
 
< 0.1%
Other values (96506) 115311
99.7%
2024-04-25T07:54:54.330103image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 231848
 
6.3%
f 231815
 
6.3%
5 231684
 
6.3%
c 231607
 
6.3%
6 231574
 
6.3%
1 231546
 
6.3%
e 231206
 
6.2%
8 231191
 
6.2%
a 231174
 
6.2%
d 231155
 
6.2%
Other values (6) 1384688
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2311390
62.5%
Lowercase Letter 1388098
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 231848
10.0%
5 231684
10.0%
6 231574
10.0%
1 231546
10.0%
8 231191
10.0%
9 231116
10.0%
3 231114
10.0%
7 231035
10.0%
4 230174
10.0%
0 230108
10.0%
Lowercase Letter
ValueCountFrequency (%)
f 231815
16.7%
c 231607
16.7%
e 231206
16.7%
a 231174
16.7%
d 231155
16.7%
b 231141
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2311390
62.5%
Latin 1388098
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 231848
10.0%
5 231684
10.0%
6 231574
10.0%
1 231546
10.0%
8 231191
10.0%
9 231116
10.0%
3 231114
10.0%
7 231035
10.0%
4 230174
10.0%
0 230108
10.0%
Latin
ValueCountFrequency (%)
f 231815
16.7%
c 231607
16.7%
e 231206
16.7%
a 231174
16.7%
d 231155
16.7%
b 231141
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 231848
 
6.3%
f 231815
 
6.3%
5 231684
 
6.3%
c 231607
 
6.3%
6 231574
 
6.3%
1 231546
 
6.3%
e 231206
 
6.2%
8 231191
 
6.2%
a 231174
 
6.2%
d 231155
 
6.2%
Other values (6) 1384688
37.4%

order_status
Categorical

IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
delivered
113210 
shipped
 
1138
canceled
 
536
invoiced
 
358
processing
 
357
Other values (2)
 
10

Length

Max length11
Median length9
Mean length8.9757631
Min length7

Characters and Unicode

Total characters1037679
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered

Common Values

ValueCountFrequency (%)
delivered 113210
97.9%
shipped 1138
 
1.0%
canceled 536
 
0.5%
invoiced 358
 
0.3%
processing 357
 
0.3%
unavailable 7
 
< 0.1%
approved 3
 
< 0.1%

Length

2024-04-25T07:54:54.405363image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-25T07:54:54.463590image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
delivered 113210
97.9%
shipped 1138
 
1.0%
canceled 536
 
0.5%
invoiced 358
 
0.3%
processing 357
 
0.3%
unavailable 7
 
< 0.1%
approved 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 342565
33.0%
d 228455
22.0%
i 115428
 
11.1%
l 113760
 
11.0%
v 113578
 
10.9%
r 113570
 
10.9%
p 2639
 
0.3%
s 1852
 
0.2%
c 1787
 
0.2%
n 1258
 
0.1%
Other values (6) 2787
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1037679
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 342565
33.0%
d 228455
22.0%
i 115428
 
11.1%
l 113760
 
11.0%
v 113578
 
10.9%
r 113570
 
10.9%
p 2639
 
0.3%
s 1852
 
0.2%
c 1787
 
0.2%
n 1258
 
0.1%
Other values (6) 2787
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1037679
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 342565
33.0%
d 228455
22.0%
i 115428
 
11.1%
l 113760
 
11.0%
v 113578
 
10.9%
r 113570
 
10.9%
p 2639
 
0.3%
s 1852
 
0.2%
c 1787
 
0.2%
n 1258
 
0.1%
Other values (6) 2787
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1037679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 342565
33.0%
d 228455
22.0%
i 115428
 
11.1%
l 113760
 
11.0%
v 113578
 
10.9%
r 113570
 
10.9%
p 2639
 
0.3%
s 1852
 
0.2%
c 1787
 
0.2%
n 1258
 
0.1%
Other values (6) 2787
 
0.3%

payment_sequential
Real number (ℝ)

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.093747
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:54.512466image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum29
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.72984871
Coefficient of variation (CV)0.66729206
Kurtosis350.44021
Mean1.093747
Median Absolute Deviation (MAD)0
Skewness16.001768
Sum126447
Variance0.53267914
MonotonicityNot monotonic
2024-04-25T07:54:54.560759image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 110662
95.7%
2 3298
 
2.9%
3 636
 
0.6%
4 307
 
0.3%
5 183
 
0.2%
6 127
 
0.1%
7 88
 
0.1%
8 58
 
0.1%
9 47
 
< 0.1%
10 40
 
< 0.1%
Other values (19) 163
 
0.1%
ValueCountFrequency (%)
1 110662
95.7%
2 3298
 
2.9%
3 636
 
0.6%
4 307
 
0.3%
5 183
 
0.2%
6 127
 
0.1%
7 88
 
0.1%
8 58
 
0.1%
9 47
 
< 0.1%
10 40
 
< 0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 2
 
< 0.1%
25 2
 
< 0.1%
24 2
 
< 0.1%
23 2
 
< 0.1%
22 3
< 0.1%
21 6
< 0.1%
20 6
< 0.1%

payment_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
credit_card
85278 
boleto
22510 
voucher
 
6162
debit_card
 
1659

Length

Max length11
Median length11
Mean length9.7989084
Min length6

Characters and Unicode

Total characters1132842
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowvoucher
3rd rowvoucher
4th rowcredit_card
5th rowcredit_card

Common Values

ValueCountFrequency (%)
credit_card 85278
73.8%
boleto 22510
 
19.5%
voucher 6162
 
5.3%
debit_card 1659
 
1.4%

Length

2024-04-25T07:54:54.611345image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-25T07:54:54.660142image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
credit_card 85278
73.8%
boleto 22510
 
19.5%
voucher 6162
 
5.3%
debit_card 1659
 
1.4%

Most occurring characters

ValueCountFrequency (%)
c 178377
15.7%
r 178377
15.7%
d 173874
15.3%
e 115609
10.2%
t 109447
9.7%
i 86937
7.7%
_ 86937
7.7%
a 86937
7.7%
o 51182
 
4.5%
b 24169
 
2.1%
Other values (4) 40996
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1045905
92.3%
Connector Punctuation 86937
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 178377
17.1%
r 178377
17.1%
d 173874
16.6%
e 115609
11.1%
t 109447
10.5%
i 86937
8.3%
a 86937
8.3%
o 51182
 
4.9%
b 24169
 
2.3%
l 22510
 
2.2%
Other values (3) 18486
 
1.8%
Connector Punctuation
ValueCountFrequency (%)
_ 86937
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1045905
92.3%
Common 86937
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 178377
17.1%
r 178377
17.1%
d 173874
16.6%
e 115609
11.1%
t 109447
10.5%
i 86937
8.3%
a 86937
8.3%
o 51182
 
4.9%
b 24169
 
2.3%
l 22510
 
2.2%
Other values (3) 18486
 
1.8%
Common
ValueCountFrequency (%)
_ 86937
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1132842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 178377
15.7%
r 178377
15.7%
d 173874
15.3%
e 115609
10.2%
t 109447
9.7%
i 86937
7.7%
_ 86937
7.7%
a 86937
7.7%
o 51182
 
4.5%
b 24169
 
2.1%
Other values (4) 40996
 
3.6%

payment_installments
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9462326
Minimum0
Maximum24
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:54.702311image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7810871
Coefficient of variation (CV)0.94394691
Kurtosis2.5137681
Mean2.9462326
Median Absolute Deviation (MAD)1
Skewness1.6181717
Sum340611
Variance7.7344456
MonotonicityNot monotonic
2024-04-25T07:54:54.751884image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 57599
49.8%
2 13404
 
11.6%
3 11551
 
10.0%
4 7855
 
6.8%
10 6785
 
5.9%
5 5928
 
5.1%
8 5013
 
4.3%
6 4546
 
3.9%
7 1789
 
1.5%
9 710
 
0.6%
Other values (14) 429
 
0.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 57599
49.8%
2 13404
 
11.6%
3 11551
 
10.0%
4 7855
 
6.8%
5 5928
 
5.1%
6 4546
 
3.9%
7 1789
 
1.5%
8 5013
 
4.3%
9 710
 
0.6%
ValueCountFrequency (%)
24 34
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 6
 
< 0.1%
20 20
 
< 0.1%
18 38
< 0.1%
17 7
 
< 0.1%
16 7
 
< 0.1%
15 91
0.1%
14 16
 
< 0.1%

payment_value
Real number (ℝ)

HIGH CORRELATION 

Distinct28657
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.38738
Minimum0
Maximum13664.08
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:54.805673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.23
Q160.87
median108.05
Q3189.48
95-th percentile514.98
Maximum13664.08
Range13664.08
Interquartile range (IQR)128.61

Descriptive statistics

Standard deviation265.87397
Coefficient of variation (CV)1.5423053
Kurtosis524.94523
Mean172.38738
Median Absolute Deviation (MAD)56.67
Skewness14.306544
Sum19929533
Variance70688.967
MonotonicityNot monotonic
2024-04-25T07:54:54.860579image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 338
 
0.3%
100 285
 
0.2%
20 283
 
0.2%
77.57 249
 
0.2%
35 163
 
0.1%
73.34 157
 
0.1%
30 134
 
0.1%
116.94 130
 
0.1%
56.78 120
 
0.1%
155.14 119
 
0.1%
Other values (28647) 113631
98.3%
ValueCountFrequency (%)
0 6
< 0.1%
0.01 6
< 0.1%
0.03 2
 
< 0.1%
0.05 2
 
< 0.1%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 3
< 0.1%
0.11 2
 
< 0.1%
0.13 1
 
< 0.1%
0.14 4
< 0.1%
ValueCountFrequency (%)
13664.08 8
< 0.1%
7274.88 4
< 0.1%
6929.31 1
 
< 0.1%
6726.66 1
 
< 0.1%
6081.54 6
< 0.1%
4950.34 1
 
< 0.1%
4809.44 2
 
< 0.1%
4764.34 1
 
< 0.1%
4681.78 1
 
< 0.1%
4513.32 1
 
< 0.1%
Distinct93396
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:54.991823image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79377 ?
Unique (%)68.7%

Sample

1st row7c396fd4830fd04220f754e42b4e5bff
2nd row7c396fd4830fd04220f754e42b4e5bff
3rd row7c396fd4830fd04220f754e42b4e5bff
4th row3a51803cc0d012c3b5dc8b7528cb05f7
5th rowef0996a1a279c26e7ecbd737be23d235
ValueCountFrequency (%)
9a736b248f67d166d2fbb006bcb877c3 75
 
0.1%
6fbc7cdadbb522125f4b27ae9dee4060 38
 
< 0.1%
f9ae226291893fda10af7965268fb7f6 35
 
< 0.1%
8af7ac63b2efbcbd88e5b11505e8098a 29
 
< 0.1%
569aa12b73b5f7edeaa6f2a01603e381 26
 
< 0.1%
db1af3fd6b23ac3873ef02619d548f9c 24
 
< 0.1%
90807fdb59eec2152bc977feeb6e47e7 24
 
< 0.1%
d97b3cfb22b0d6b25ac9ed4e9c2d481b 24
 
< 0.1%
1d2435aa3b858d45c707c9fc25e18779 24
 
< 0.1%
5419a7c9b86a43d8140e2939cd2c2f7e 24
 
< 0.1%
Other values (93386) 115286
99.7%
2024-04-25T07:54:55.175454image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 232253
 
6.3%
b 231885
 
6.3%
1 231813
 
6.3%
a 231488
 
6.3%
d 231353
 
6.3%
3 231341
 
6.3%
e 231271
 
6.3%
8 231179
 
6.2%
2 231112
 
6.2%
5 231087
 
6.2%
Other values (6) 1384706
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2312012
62.5%
Lowercase Letter 1387476
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 232253
10.0%
1 231813
10.0%
3 231341
10.0%
8 231179
10.0%
2 231112
10.0%
5 231087
10.0%
9 231084
10.0%
7 231012
10.0%
0 230821
10.0%
4 230310
10.0%
Lowercase Letter
ValueCountFrequency (%)
b 231885
16.7%
a 231488
16.7%
d 231353
16.7%
e 231271
16.7%
f 230923
16.6%
c 230556
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2312012
62.5%
Latin 1387476
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 232253
10.0%
1 231813
10.0%
3 231341
10.0%
8 231179
10.0%
2 231112
10.0%
5 231087
10.0%
9 231084
10.0%
7 231012
10.0%
0 230821
10.0%
4 230310
10.0%
Latin
ValueCountFrequency (%)
b 231885
16.7%
a 231488
16.7%
d 231353
16.7%
e 231271
16.7%
f 230923
16.6%
c 230556
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 232253
 
6.3%
b 231885
 
6.3%
1 231813
 
6.3%
a 231488
 
6.3%
d 231353
 
6.3%
3 231341
 
6.3%
e 231271
 
6.3%
8 231179
 
6.2%
2 231112
 
6.2%
5 231087
 
6.2%
Other values (6) 1384706
37.4%

customer_zip_code_prefix
Real number (ℝ)

HIGH CORRELATION 

Distinct14907
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35061.538
Minimum1003
Maximum99980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:55.250243image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1003
5-th percentile3286
Q111310
median24241
Q358745
95-th percentile90620
Maximum99980
Range98977
Interquartile range (IQR)47435

Descriptive statistics

Standard deviation29841.672
Coefficient of variation (CV)0.85112273
Kurtosis-0.78410177
Mean35061.538
Median Absolute Deviation (MAD)16231
Skewness0.78436422
Sum4.0534293 × 109
Variance8.9052537 × 108
MonotonicityNot monotonic
2024-04-25T07:54:55.309158image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24220 154
 
0.1%
22793 151
 
0.1%
22790 150
 
0.1%
24230 138
 
0.1%
22775 126
 
0.1%
35162 124
 
0.1%
29101 112
 
0.1%
11740 110
 
0.1%
13087 107
 
0.1%
36570 104
 
0.1%
Other values (14897) 114333
98.9%
ValueCountFrequency (%)
1003 1
 
< 0.1%
1004 2
 
< 0.1%
1005 6
< 0.1%
1006 2
 
< 0.1%
1007 4
< 0.1%
1008 3
 
< 0.1%
1009 8
< 0.1%
1011 6
< 0.1%
1012 2
 
< 0.1%
1013 3
 
< 0.1%
ValueCountFrequency (%)
99980 3
 
< 0.1%
99970 1
 
< 0.1%
99965 2
 
< 0.1%
99960 1
 
< 0.1%
99955 3
 
< 0.1%
99950 9
< 0.1%
99940 2
 
< 0.1%
99930 5
< 0.1%
99925 1
 
< 0.1%
99920 1
 
< 0.1%
Distinct4093
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:55.415540image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length32
Median length27
Mean length10.332855
Min length3

Characters and Unicode

Total characters1194571
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1042 ?
Unique (%)0.9%

Sample

1st rowsao paulo
2nd rowsao paulo
3rd rowsao paulo
4th rowsao paulo
5th rowsao paulo
ValueCountFrequency (%)
sao 24620
 
12.1%
paulo 18348
 
9.1%
de 11267
 
5.6%
rio 9627
 
4.8%
janeiro 8022
 
4.0%
do 4964
 
2.4%
belo 3269
 
1.6%
horizonte 3224
 
1.6%
brasilia 2444
 
1.2%
porto 1942
 
1.0%
Other values (3268) 114916
56.7%
2024-04-25T07:54:55.592988image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 197038
16.5%
o 147371
12.3%
i 91350
 
7.6%
r 88530
 
7.4%
87034
 
7.3%
e 77539
 
6.5%
s 73205
 
6.1%
n 52923
 
4.4%
u 52440
 
4.4%
l 52072
 
4.4%
Other values (21) 275069
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1106996
92.7%
Space Separator 87034
 
7.3%
Dash Punctuation 281
 
< 0.1%
Other Punctuation 258
 
< 0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 197038
17.8%
o 147371
13.3%
i 91350
 
8.3%
r 88530
 
8.0%
e 77539
 
7.0%
s 73205
 
6.6%
n 52923
 
4.8%
u 52440
 
4.7%
l 52072
 
4.7%
p 43443
 
3.9%
Other values (16) 231085
20.9%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
87034
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 281
100.0%
Other Punctuation
ValueCountFrequency (%)
' 258
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1106996
92.7%
Common 87575
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 197038
17.8%
o 147371
13.3%
i 91350
 
8.3%
r 88530
 
8.0%
e 77539
 
7.0%
s 73205
 
6.6%
n 52923
 
4.8%
u 52440
 
4.7%
l 52072
 
4.7%
p 43443
 
3.9%
Other values (16) 231085
20.9%
Common
ValueCountFrequency (%)
87034
99.4%
- 281
 
0.3%
' 258
 
0.3%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 197038
16.5%
o 147371
12.3%
i 91350
 
7.6%
r 88530
 
7.4%
87034
 
7.3%
e 77539
 
6.5%
s 73205
 
6.1%
n 52923
 
4.4%
u 52440
 
4.4%
l 52072
 
4.4%
Other values (21) 275069
23.0%

customer_state
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
SP
48797 
RJ
14987 
MG
13429 
RS
6413 
PR
5879 
Other values (22)
26104 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters231218
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowSP
3rd rowSP
4th rowSP
5th rowSP

Common Values

ValueCountFrequency (%)
SP 48797
42.2%
RJ 14987
 
13.0%
MG 13429
 
11.6%
RS 6413
 
5.5%
PR 5879
 
5.1%
SC 4218
 
3.6%
BA 3942
 
3.4%
DF 2449
 
2.1%
GO 2359
 
2.0%
ES 2300
 
2.0%
Other values (17) 10836
 
9.4%

Length

2024-04-25T07:54:55.659517image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 48797
42.2%
rj 14987
 
13.0%
mg 13429
 
11.6%
rs 6413
 
5.5%
pr 5879
 
5.1%
sc 4218
 
3.6%
ba 3942
 
3.4%
df 2449
 
2.1%
go 2359
 
2.0%
es 2300
 
2.0%
Other values (17) 10836
 
9.4%

Most occurring characters

ValueCountFrequency (%)
S 62966
27.2%
P 58871
25.5%
R 28218
12.2%
M 16380
 
7.1%
G 15788
 
6.8%
J 14987
 
6.5%
A 6654
 
2.9%
E 6071
 
2.6%
C 5838
 
2.5%
B 4561
 
2.0%
Other values (7) 10884
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 231218
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 62966
27.2%
P 58871
25.5%
R 28218
12.2%
M 16380
 
7.1%
G 15788
 
6.8%
J 14987
 
6.5%
A 6654
 
2.9%
E 6071
 
2.6%
C 5838
 
2.5%
B 4561
 
2.0%
Other values (7) 10884
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 231218
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 62966
27.2%
P 58871
25.5%
R 28218
12.2%
M 16380
 
7.1%
G 15788
 
6.8%
J 14987
 
6.5%
A 6654
 
2.9%
E 6071
 
2.6%
C 5838
 
2.5%
B 4561
 
2.0%
Other values (7) 10884
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231218
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 62966
27.2%
P 58871
25.5%
R 28218
12.2%
M 16380
 
7.1%
G 15788
 
6.8%
J 14987
 
6.5%
A 6654
 
2.9%
E 6071
 
2.6%
C 5838
 
2.5%
B 4561
 
2.0%
Other values (7) 10884
 
4.7%

order_item_id
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.194535
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:55.704022image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.68592588
Coefficient of variation (CV)0.57421998
Kurtosis93.440808
Mean1.194535
Median Absolute Deviation (MAD)0
Skewness7.195206
Sum138099
Variance0.47049432
MonotonicityNot monotonic
2024-04-25T07:54:55.751695image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 101340
87.7%
2 10055
 
8.7%
3 2326
 
2.0%
4 969
 
0.8%
5 458
 
0.4%
6 256
 
0.2%
7 60
 
0.1%
8 35
 
< 0.1%
9 28
 
< 0.1%
10 25
 
< 0.1%
Other values (11) 57
 
< 0.1%
ValueCountFrequency (%)
1 101340
87.7%
2 10055
 
8.7%
3 2326
 
2.0%
4 969
 
0.8%
5 458
 
0.4%
6 256
 
0.2%
7 60
 
0.1%
8 35
 
< 0.1%
9 28
 
< 0.1%
10 25
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 2
 
< 0.1%
19 2
 
< 0.1%
18 2
 
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
15 4
 
< 0.1%
14 6
< 0.1%
13 7
< 0.1%
12 12
< 0.1%
Distinct32171
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:55.848081image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16932 ?
Unique (%)14.6%

Sample

1st row87285b34884572647811a353c7ac498a
2nd row87285b34884572647811a353c7ac498a
3rd row87285b34884572647811a353c7ac498a
4th row87285b34884572647811a353c7ac498a
5th row87285b34884572647811a353c7ac498a
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 533
 
0.5%
99a4788cb24856965c36a24e339b6058 517
 
0.4%
422879e10f46682990de24d770e7f83d 507
 
0.4%
389d119b48cf3043d311335e499d9c6b 405
 
0.4%
368c6c730842d78016ad823897a372db 395
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 389
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 354
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 324
 
0.3%
154e7e31ebfa092203795c972e5804a6 294
 
0.3%
3dd2a17168ec895c781a9191c1e95ad7 276
 
0.2%
Other values (32161) 111615
96.5%
2024-04-25T07:54:56.006521image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 237911
 
6.4%
9 235951
 
6.4%
e 233656
 
6.3%
7 233105
 
6.3%
8 232719
 
6.3%
4 231409
 
6.3%
a 231308
 
6.3%
c 231075
 
6.2%
2 230954
 
6.2%
6 230872
 
6.2%
Other values (6) 1370528
37.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2321899
62.8%
Lowercase Letter 1377589
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 237911
10.2%
9 235951
10.2%
7 233105
10.0%
8 232719
10.0%
4 231409
10.0%
2 230954
9.9%
6 230872
9.9%
0 230822
9.9%
5 229692
9.9%
1 228464
9.8%
Lowercase Letter
ValueCountFrequency (%)
e 233656
17.0%
a 231308
16.8%
c 231075
16.8%
b 229587
16.7%
d 227350
16.5%
f 224613
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2321899
62.8%
Latin 1377589
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3 237911
10.2%
9 235951
10.2%
7 233105
10.0%
8 232719
10.0%
4 231409
10.0%
2 230954
9.9%
6 230872
9.9%
0 230822
9.9%
5 229692
9.9%
1 228464
9.8%
Latin
ValueCountFrequency (%)
e 233656
17.0%
a 231308
16.8%
c 231075
16.8%
b 229587
16.7%
d 227350
16.5%
f 224613
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 237911
 
6.4%
9 235951
 
6.4%
e 233656
 
6.3%
7 233105
 
6.3%
8 232719
 
6.3%
4 231409
 
6.3%
a 231308
 
6.3%
c 231075
 
6.2%
2 230954
 
6.2%
6 230872
 
6.2%
Other values (6) 1370528
37.0%
Distinct3028
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:56.105393image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3699488
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique485 ?
Unique (%)0.4%

Sample

1st row3504c0cb71d7fa48d967e0e4c94d59d9
2nd row3504c0cb71d7fa48d967e0e4c94d59d9
3rd row3504c0cb71d7fa48d967e0e4c94d59d9
4th row3504c0cb71d7fa48d967e0e4c94d59d9
5th row3504c0cb71d7fa48d967e0e4c94d59d9
ValueCountFrequency (%)
4a3ca9315b744ce9f8e9374361493884 2128
 
1.8%
6560211a19b47992c3666cc44a7e94c0 2111
 
1.8%
1f50f920176fa81dab994f9023523100 2009
 
1.7%
cc419e0650a3c5ba77189a1882b7556a 1885
 
1.6%
da8622b14eb17ae2831f4ac5b9dab84a 1656
 
1.4%
955fee9216a65b617aa5c0531780ce60 1517
 
1.3%
1025f0e2d44d7041d6cf58b6550e0bfa 1465
 
1.3%
7c67e1448b00f6e969d365cea6b010ab 1454
 
1.3%
7a67c85e85bb2ce8582c35f2203ad736 1236
 
1.1%
ea8482cd71df3c1969d7b9473ff13abc 1233
 
1.1%
Other values (3018) 98915
85.6%
2024-04-25T07:54:56.248868image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 251950
 
6.8%
c 243876
 
6.6%
4 243091
 
6.6%
6 238193
 
6.4%
0 237394
 
6.4%
a 236721
 
6.4%
b 235816
 
6.4%
3 235558
 
6.4%
9 228810
 
6.2%
2 227464
 
6.1%
Other values (6) 1320615
35.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2339688
63.2%
Lowercase Letter 1359800
36.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 251950
10.8%
4 243091
10.4%
6 238193
10.2%
0 237394
10.1%
3 235558
10.1%
9 228810
9.8%
2 227464
9.7%
8 226366
9.7%
5 225942
9.7%
7 224920
9.6%
Lowercase Letter
ValueCountFrequency (%)
c 243876
17.9%
a 236721
17.4%
b 235816
17.3%
e 216428
15.9%
f 214706
15.8%
d 212253
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2339688
63.2%
Latin 1359800
36.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 251950
10.8%
4 243091
10.4%
6 238193
10.2%
0 237394
10.1%
3 235558
10.1%
9 228810
9.8%
2 227464
9.7%
8 226366
9.7%
5 225942
9.7%
7 224920
9.6%
Latin
ValueCountFrequency (%)
c 243876
17.9%
a 236721
17.4%
b 235816
17.3%
e 216428
15.9%
f 214706
15.8%
d 212253
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3699488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 251950
 
6.8%
c 243876
 
6.6%
4 243091
 
6.6%
6 238193
 
6.4%
0 237394
 
6.4%
a 236721
 
6.4%
b 235816
 
6.4%
3 235558
 
6.4%
9 228810
 
6.2%
2 227464
 
6.1%
Other values (6) 1320615
35.7%
Distinct91386
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
Minimum2016-09-19 00:15:34
Maximum2020-04-09 22:35:08
2024-04-25T07:54:56.324329image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:56.490287image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

HIGH CORRELATION 

Distinct5879
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.61985
Minimum0.85
Maximum6735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:56.548954image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile17
Q139.9
median74.9
Q3134.9
95-th percentile349.9
Maximum6735
Range6734.15
Interquartile range (IQR)95

Descriptive statistics

Standard deviation182.65348
Coefficient of variation (CV)1.5142904
Kurtosis107.9051
Mean120.61985
Median Absolute Deviation (MAD)42
Skewness7.6154182
Sum13944740
Variance33362.292
MonotonicityNot monotonic
2024-04-25T07:54:56.603905image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9 2574
 
2.2%
69.9 2096
 
1.8%
49.9 2013
 
1.7%
89.9 1604
 
1.4%
99.9 1505
 
1.3%
29.9 1362
 
1.2%
39.9 1325
 
1.1%
79.9 1266
 
1.1%
19.9 1263
 
1.1%
29.99 1204
 
1.0%
Other values (5869) 99397
86.0%
ValueCountFrequency (%)
0.85 3
 
< 0.1%
1.2 20
< 0.1%
2.2 2
 
< 0.1%
2.29 1
 
< 0.1%
2.9 1
 
< 0.1%
2.99 1
 
< 0.1%
3.06 3
 
< 0.1%
3.49 3
 
< 0.1%
3.5 7
 
< 0.1%
3.54 1
 
< 0.1%
ValueCountFrequency (%)
6735 1
< 0.1%
6499 1
< 0.1%
4799 1
< 0.1%
4690 1
< 0.1%
4590 1
< 0.1%
4399.87 1
< 0.1%
4099.99 1
< 0.1%
4059 1
< 0.1%
3999.9 1
< 0.1%
3999 2
< 0.1%

freight_value
Real number (ℝ)

Distinct6954
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.05688
Minimum0
Maximum409.68
Zeros387
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:56.664124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.78
Q113.08
median16.32
Q321.21
95-th percentile45.31
Maximum409.68
Range409.68
Interquartile range (IQR)8.13

Descriptive statistics

Standard deviation15.836184
Coefficient of variation (CV)0.78956371
Kurtosis58.250048
Mean20.05688
Median Absolute Deviation (MAD)3.63
Skewness5.5602128
Sum2318755.8
Variance250.78473
MonotonicityNot monotonic
2024-04-25T07:54:56.718861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 3754
 
3.2%
7.78 2281
 
2.0%
11.85 1948
 
1.7%
14.1 1939
 
1.7%
18.23 1599
 
1.4%
7.39 1554
 
1.3%
16.11 1185
 
1.0%
15.23 1041
 
0.9%
8.72 950
 
0.8%
16.79 902
 
0.8%
Other values (6944) 98456
85.2%
ValueCountFrequency (%)
0 387
0.3%
0.01 4
 
< 0.1%
0.02 3
 
< 0.1%
0.03 14
 
< 0.1%
0.04 4
 
< 0.1%
0.05 3
 
< 0.1%
0.06 13
 
< 0.1%
0.07 1
 
< 0.1%
0.08 12
 
< 0.1%
0.09 6
 
< 0.1%
ValueCountFrequency (%)
409.68 1
< 0.1%
375.28 2
< 0.1%
339.59 1
< 0.1%
338.3 1
< 0.1%
322.1 1
< 0.1%
321.88 1
< 0.1%
321.46 1
< 0.1%
317.47 1
< 0.1%
314.4 1
< 0.1%
314.02 1
< 0.1%
Distinct71
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:56.788158image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length46
Median length30
Mean length14.875062
Min length3

Characters and Unicode

Total characters1719691
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowutilidades_domesticas
2nd rowutilidades_domesticas
3rd rowutilidades_domesticas
4th rowutilidades_domesticas
5th rowutilidades_domesticas
ValueCountFrequency (%)
cama_mesa_banho 11847
 
10.2%
beleza_saude 9944
 
8.6%
esporte_lazer 8942
 
7.7%
moveis_decoracao 8743
 
7.6%
informatica_acessorios 8105
 
7.0%
utilidades_domesticas 7331
 
6.3%
relogios_presentes 6161
 
5.3%
telefonia 4692
 
4.1%
ferramentas_jardim 4558
 
3.9%
automotivo 4356
 
3.8%
Other values (61) 40930
35.4%
2024-04-25T07:54:56.927225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 208749
12.1%
a 206939
12.0%
s 171469
10.0%
o 170145
9.9%
i 114273
 
6.6%
r 110581
 
6.4%
_ 109454
 
6.4%
t 82596
 
4.8%
c 81711
 
4.8%
m 77941
 
4.5%
Other values (18) 385833
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1609941
93.6%
Connector Punctuation 109454
 
6.4%
Decimal Number 296
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 208749
13.0%
a 206939
12.9%
s 171469
10.7%
o 170145
10.6%
i 114273
 
7.1%
r 110581
 
6.9%
t 82596
 
5.1%
c 81711
 
5.1%
m 77941
 
4.8%
n 58609
 
3.6%
Other values (16) 326928
20.3%
Connector Punctuation
ValueCountFrequency (%)
_ 109454
100.0%
Decimal Number
ValueCountFrequency (%)
2 296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1609941
93.6%
Common 109750
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 208749
13.0%
a 206939
12.9%
s 171469
10.7%
o 170145
10.6%
i 114273
 
7.1%
r 110581
 
6.9%
t 82596
 
5.1%
c 81711
 
5.1%
m 77941
 
4.8%
n 58609
 
3.6%
Other values (16) 326928
20.3%
Common
ValueCountFrequency (%)
_ 109454
99.7%
2 296
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1719691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 208749
12.1%
a 206939
12.0%
s 171469
10.0%
o 170145
9.9%
i 114273
 
6.6%
r 110581
 
6.4%
_ 109454
 
6.4%
t 82596
 
4.8%
c 81711
 
4.8%
m 77941
 
4.5%
Other values (18) 385833
22.4%

product_name_lenght
Real number (ℝ)

Distinct66
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.766541
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:56.996706image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q142
median52
Q357
95-th percentile60
Maximum76
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.034187
Coefficient of variation (CV)0.20575966
Kurtosis0.14955291
Mean48.766541
Median Absolute Deviation (MAD)6
Skewness-0.90541969
Sum5637851
Variance100.68491
MonotonicityNot monotonic
2024-04-25T07:54:57.055093image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 8598
 
7.4%
60 8004
 
6.9%
56 6803
 
5.9%
58 6753
 
5.8%
57 6261
 
5.4%
55 5797
 
5.0%
54 5469
 
4.7%
53 4319
 
3.7%
52 4280
 
3.7%
49 3670
 
3.2%
Other values (56) 55655
48.1%
ValueCountFrequency (%)
5 9
 
< 0.1%
6 3
 
< 0.1%
7 2
 
< 0.1%
8 4
 
< 0.1%
9 14
 
< 0.1%
10 8
 
< 0.1%
11 11
 
< 0.1%
12 37
< 0.1%
13 26
< 0.1%
14 47
< 0.1%
ValueCountFrequency (%)
76 1
 
< 0.1%
72 9
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 3
 
< 0.1%
66 1
 
< 0.1%
64 170
 
0.1%
63 1335
1.2%
62 160
 
0.1%
61 239
 
0.2%
Distinct2958
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean785.8082
Minimum4
Maximum3992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:57.119617image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile160
Q1346
median600
Q3983
95-th percentile2120
Maximum3992
Range3988
Interquartile range (IQR)637

Descriptive statistics

Standard deviation652.41862
Coefficient of variation (CV)0.83025173
Kurtosis4.9272446
Mean785.8082
Median Absolute Deviation (MAD)295
Skewness2.011533
Sum90846500
Variance425650.05
MonotonicityNot monotonic
2024-04-25T07:54:57.184762image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
341 708
 
0.6%
1893 664
 
0.6%
348 643
 
0.6%
492 590
 
0.5%
903 588
 
0.5%
245 576
 
0.5%
366 532
 
0.5%
236 513
 
0.4%
340 484
 
0.4%
919 436
 
0.4%
Other values (2948) 109875
95.0%
ValueCountFrequency (%)
4 6
< 0.1%
8 2
 
< 0.1%
15 1
 
< 0.1%
20 7
< 0.1%
23 1
 
< 0.1%
26 2
 
< 0.1%
27 4
< 0.1%
28 2
 
< 0.1%
30 8
< 0.1%
31 2
 
< 0.1%
ValueCountFrequency (%)
3992 2
 
< 0.1%
3988 1
 
< 0.1%
3985 3
< 0.1%
3976 6
< 0.1%
3963 1
 
< 0.1%
3956 3
< 0.1%
3954 2
 
< 0.1%
3950 2
 
< 0.1%
3949 1
 
< 0.1%
3948 1
 
< 0.1%

product_photos_qty
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2053733
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:57.238342image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.717771
Coefficient of variation (CV)0.7789026
Kurtosis4.8372556
Mean2.2053733
Median Absolute Deviation (MAD)0
Skewness1.910868
Sum254961
Variance2.9507372
MonotonicityNot monotonic
2024-04-25T07:54:57.285115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 58429
50.5%
2 22894
 
19.8%
3 12869
 
11.1%
4 8770
 
7.6%
5 5558
 
4.8%
6 3905
 
3.4%
7 1552
 
1.3%
8 771
 
0.7%
10 353
 
0.3%
9 309
 
0.3%
Other values (9) 199
 
0.2%
ValueCountFrequency (%)
1 58429
50.5%
2 22894
 
19.8%
3 12869
 
11.1%
4 8770
 
7.6%
5 5558
 
4.8%
6 3905
 
3.4%
7 1552
 
1.3%
8 771
 
0.7%
9 309
 
0.3%
10 353
 
0.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
< 0.1%
17 11
 
< 0.1%
15 12
 
< 0.1%
14 6
 
< 0.1%
13 30
 
< 0.1%
12 60
 
0.1%
11 73
 
0.1%
10 353
0.3%

product_weight_g
Real number (ℝ)

HIGH CORRELATION 

Distinct2197
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2113.8911
Minimum0
Maximum40425
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:57.344058image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1300
median700
Q31800
95-th percentile9850
Maximum40425
Range40425
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3781.7427
Coefficient of variation (CV)1.788996
Kurtosis16.034687
Mean2113.8911
Median Absolute Deviation (MAD)500
Skewness3.5806654
Sum2.4438484 × 108
Variance14301578
MonotonicityNot monotonic
2024-04-25T07:54:57.403036image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 6816
 
5.9%
150 5329
 
4.6%
250 4671
 
4.0%
300 4313
 
3.7%
100 3571
 
3.1%
400 3481
 
3.0%
350 3211
 
2.8%
500 2787
 
2.4%
600 2778
 
2.4%
700 2095
 
1.8%
Other values (2187) 76557
66.2%
ValueCountFrequency (%)
0 8
 
< 0.1%
2 5
 
< 0.1%
25 3
 
< 0.1%
50 985
0.9%
53 2
 
< 0.1%
54 2
 
< 0.1%
55 2
 
< 0.1%
58 1
 
< 0.1%
60 9
 
< 0.1%
61 5
 
< 0.1%
ValueCountFrequency (%)
40425 3
 
< 0.1%
30000 296
0.3%
29800 1
 
< 0.1%
29750 1
 
< 0.1%
29700 4
 
< 0.1%
29600 5
 
< 0.1%
29500 2
 
< 0.1%
29250 1
 
< 0.1%
29150 1
 
< 0.1%
29100 1
 
< 0.1%

product_length_cm
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.307779
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:57.464561image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile62
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.211093
Coefficient of variation (CV)0.53488225
Kurtosis3.6631492
Mean30.307779
Median Absolute Deviation (MAD)8
Skewness1.7425168
Sum3503852
Variance262.79953
MonotonicityNot monotonic
2024-04-25T07:54:57.520980image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 17713
 
15.3%
20 10603
 
9.2%
30 7814
 
6.8%
17 6126
 
5.3%
18 5844
 
5.1%
19 4845
 
4.2%
25 4768
 
4.1%
40 4238
 
3.7%
22 3935
 
3.4%
50 3091
 
2.7%
Other values (89) 46632
40.3%
ValueCountFrequency (%)
7 32
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 8
 
< 0.1%
11 96
 
0.1%
12 41
 
< 0.1%
13 60
 
0.1%
14 137
 
0.1%
15 212
 
0.2%
16 17713
15.3%
ValueCountFrequency (%)
105 331
0.3%
104 30
 
< 0.1%
103 45
 
< 0.1%
102 60
 
0.1%
101 108
 
0.1%
100 424
0.4%
99 36
 
< 0.1%
98 50
 
< 0.1%
97 11
 
< 0.1%
96 8
 
< 0.1%

product_height_cm
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.638419
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:57.579680image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320
95-th percentile45
Maximum105
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.473526
Coefficient of variation (CV)0.80978399
Kurtosis7.2875923
Mean16.638419
Median Absolute Deviation (MAD)6
Skewness2.2431354
Sum1923551
Variance181.53589
MonotonicityNot monotonic
2024-04-25T07:54:57.638644image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 10209
 
8.8%
20 6796
 
5.9%
15 6784
 
5.9%
11 6309
 
5.5%
12 6196
 
5.4%
2 5097
 
4.4%
4 4778
 
4.1%
8 4773
 
4.1%
16 4661
 
4.0%
5 4636
 
4.0%
Other values (92) 55370
47.9%
ValueCountFrequency (%)
2 5097
4.4%
3 2770
 
2.4%
4 4778
4.1%
5 4636
4.0%
6 3511
 
3.0%
7 4217
3.6%
8 4773
4.1%
9 3369
 
2.9%
10 10209
8.8%
11 6309
5.5%
ValueCountFrequency (%)
105 137
0.1%
104 14
 
< 0.1%
103 49
 
< 0.1%
102 10
 
< 0.1%
100 42
 
< 0.1%
99 5
 
< 0.1%
98 3
 
< 0.1%
97 2
 
< 0.1%
96 8
 
< 0.1%
95 22
 
< 0.1%

product_width_cm
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.11314
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:57.696810image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.755036
Coefficient of variation (CV)0.50858672
Kurtosis4.5532615
Mean23.11314
Median Absolute Deviation (MAD)6
Skewness1.7072296
Sum2672087
Variance138.18087
MonotonicityNot monotonic
2024-04-25T07:54:57.750752image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 12448
 
10.8%
11 10662
 
9.2%
15 8964
 
7.8%
16 8677
 
7.5%
30 7880
 
6.8%
12 5590
 
4.8%
13 5412
 
4.7%
14 4753
 
4.1%
18 4122
 
3.6%
40 4042
 
3.5%
Other values (85) 43059
37.2%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 5
 
< 0.1%
8 29
 
< 0.1%
9 51
 
< 0.1%
10 83
 
0.1%
11 10662
9.2%
12 5590
4.8%
13 5412
4.7%
14 4753
4.1%
15 8964
7.8%
ValueCountFrequency (%)
118 7
 
< 0.1%
105 14
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 43
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
95 2
 
< 0.1%
Distinct71
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:57.825681image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length39
Median length31
Mean length12.989949
Min length3

Characters and Unicode

Total characters1501755
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousewares
2nd rowhousewares
3rd rowhousewares
4th rowhousewares
5th rowhousewares
ValueCountFrequency (%)
bed_bath_table 11847
 
10.2%
health_beauty 9944
 
8.6%
sports_leisure 8942
 
7.7%
furniture_decor 8743
 
7.6%
computers_accessories 8105
 
7.0%
housewares 7331
 
6.3%
watches_gifts 6161
 
5.3%
telephony 4692
 
4.1%
garden_tools 4558
 
3.9%
auto 4356
 
3.8%
Other values (61) 40930
35.4%
2024-04-25T07:54:57.973642image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 183852
12.2%
s 141050
 
9.4%
t 132154
 
8.8%
o 111010
 
7.4%
r 105008
 
7.0%
a 101597
 
6.8%
_ 101311
 
6.7%
u 77584
 
5.2%
c 71956
 
4.8%
i 62806
 
4.2%
Other values (15) 413427
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1400148
93.2%
Connector Punctuation 101311
 
6.7%
Decimal Number 296
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 183852
13.1%
s 141050
 
10.1%
t 132154
 
9.4%
o 111010
 
7.9%
r 105008
 
7.5%
a 101597
 
7.3%
u 77584
 
5.5%
c 71956
 
5.1%
i 62806
 
4.5%
h 59142
 
4.2%
Other values (13) 353989
25.3%
Connector Punctuation
ValueCountFrequency (%)
_ 101311
100.0%
Decimal Number
ValueCountFrequency (%)
2 296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1400148
93.2%
Common 101607
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 183852
13.1%
s 141050
 
10.1%
t 132154
 
9.4%
o 111010
 
7.9%
r 105008
 
7.5%
a 101597
 
7.3%
u 77584
 
5.5%
c 71956
 
5.1%
i 62806
 
4.5%
h 59142
 
4.2%
Other values (13) 353989
25.3%
Common
ValueCountFrequency (%)
_ 101311
99.7%
2 296
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1501755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 183852
12.2%
s 141050
 
9.4%
t 132154
 
8.8%
o 111010
 
7.4%
r 105008
 
7.0%
a 101597
 
6.8%
_ 101311
 
6.7%
u 77584
 
5.2%
c 71956
 
4.8%
i 62806
 
4.2%
Other values (15) 413427
27.5%

review_score
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
5
65374 
4
21951 
1
14546 
3
9718 
2
 
4020

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters115609
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row5

Common Values

ValueCountFrequency (%)
5 65374
56.5%
4 21951
 
19.0%
1 14546
 
12.6%
3 9718
 
8.4%
2 4020
 
3.5%

Length

2024-04-25T07:54:58.038805image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-25T07:54:58.089070image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
5 65374
56.5%
4 21951
 
19.0%
1 14546
 
12.6%
3 9718
 
8.4%
2 4020
 
3.5%

Most occurring characters

ValueCountFrequency (%)
5 65374
56.5%
4 21951
 
19.0%
1 14546
 
12.6%
3 9718
 
8.4%
2 4020
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 115609
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 65374
56.5%
4 21951
 
19.0%
1 14546
 
12.6%
3 9718
 
8.4%
2 4020
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 115609
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 65374
56.5%
4 21951
 
19.0%
1 14546
 
12.6%
3 9718
 
8.4%
2 4020
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115609
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 65374
56.5%
4 21951
 
19.0%
1 14546
 
12.6%
3 9718
 
8.4%
2 4020
 
3.5%
Distinct35177
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Memory size903.3 KiB
2024-04-25T07:54:58.210375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length208
Median length10
Mean length35.468545
Min length1

Characters and Unicode

Total characters4100483
Distinct characters205
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28730 ?
Unique (%)24.9%

Sample

1st rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
2nd rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
3rd rowNão testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
4th rowDeveriam embalar melhor o produto. A caixa veio toda amassada e vou dar de presente.
5th rowSó achei ela pequena pra seis xícaras ,mais é um bom produto
ValueCountFrequency (%)
no 72661
 
10.1%
reviews 66703
 
9.3%
o 22075
 
3.1%
produto 20487
 
2.8%
e 19397
 
2.7%
a 14735
 
2.0%
de 14075
 
2.0%
do 12785
 
1.8%
não 12702
 
1.8%
que 10115
 
1.4%
Other values (19330) 453824
63.1%
2024-04-25T07:54:58.418222image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
610307
14.9%
e 462494
 
11.3%
o 405605
 
9.9%
a 272669
 
6.6%
r 260310
 
6.3%
i 225166
 
5.5%
s 196336
 
4.8%
t 156573
 
3.8%
d 145458
 
3.5%
n 133224
 
3.2%
Other values (195) 1232341
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3129103
76.3%
Space Separator 610307
 
14.9%
Uppercase Letter 229112
 
5.6%
Other Punctuation 93611
 
2.3%
Decimal Number 21346
 
0.5%
Control 13578
 
0.3%
Dash Punctuation 943
 
< 0.1%
Close Punctuation 718
 
< 0.1%
Open Punctuation 701
 
< 0.1%
Other Symbol 690
 
< 0.1%
Other values (5) 374
 
< 0.1%

Most frequent character per category

Other Symbol
ValueCountFrequency (%)
👏 241
34.9%
👍 86
 
12.5%
😍 74
 
10.7%
° 27
 
3.9%
😉 22
 
3.2%
😘 20
 
2.9%
😆 19
 
2.8%
😡 18
 
2.6%
😁 13
 
1.9%
👎 13
 
1.9%
Other values (51) 157
22.8%
Lowercase Letter
ValueCountFrequency (%)
e 462494
14.8%
o 405605
13.0%
a 272669
 
8.7%
r 260310
 
8.3%
i 225166
 
7.2%
s 196336
 
6.3%
t 156573
 
5.0%
d 145458
 
4.6%
n 133224
 
4.3%
m 124354
 
4.0%
Other values (40) 746914
23.9%
Uppercase Letter
ValueCountFrequency (%)
N 75942
33.1%
E 19186
 
8.4%
O 18074
 
7.9%
A 16813
 
7.3%
P 12154
 
5.3%
R 11806
 
5.2%
C 9540
 
4.2%
M 9162
 
4.0%
S 8085
 
3.5%
T 7584
 
3.3%
Other values (31) 40766
17.8%
Other Punctuation
ValueCountFrequency (%)
. 49261
52.6%
, 27288
29.2%
! 12446
 
13.3%
/ 1787
 
1.9%
? 1566
 
1.7%
" 410
 
0.4%
: 301
 
0.3%
; 222
 
0.2%
% 192
 
0.2%
* 72
 
0.1%
Other values (5) 66
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 4919
23.0%
1 4891
22.9%
2 4095
19.2%
3 1917
 
9.0%
4 1328
 
6.2%
5 1223
 
5.7%
8 921
 
4.3%
6 877
 
4.1%
7 717
 
3.4%
9 458
 
2.1%
Math Symbol
ValueCountFrequency (%)
+ 88
61.1%
= 27
 
18.8%
| 12
 
8.3%
< 9
 
6.2%
~ 3
 
2.1%
× 2
 
1.4%
> 2
 
1.4%
÷ 1
 
0.7%
Modifier Symbol
ValueCountFrequency (%)
🏻 34
33.3%
´ 26
25.5%
🏼 15
14.7%
🏽 13
 
12.7%
^ 8
 
7.8%
🏾 4
 
3.9%
` 2
 
2.0%
Control
ValueCountFrequency (%)
6779
49.9%
6779
49.9%
20
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 715
99.6%
] 3
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 695
99.1%
[ 6
 
0.9%
Other Letter
ValueCountFrequency (%)
º 24
55.8%
ª 19
44.2%
Space Separator
ValueCountFrequency (%)
610307
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 943
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 77
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3358258
81.9%
Common 742225
 
18.1%

Most frequent character per script

Common
ValueCountFrequency (%)
610307
82.2%
. 49261
 
6.6%
, 27288
 
3.7%
! 12446
 
1.7%
6779
 
0.9%
6779
 
0.9%
0 4919
 
0.7%
1 4891
 
0.7%
2 4095
 
0.6%
3 1917
 
0.3%
Other values (102) 13543
 
1.8%
Latin
ValueCountFrequency (%)
e 462494
13.8%
o 405605
12.1%
a 272669
 
8.1%
r 260310
 
7.8%
i 225166
 
6.7%
s 196336
 
5.8%
t 156573
 
4.7%
d 145458
 
4.3%
n 133224
 
4.0%
m 124354
 
3.7%
Other values (83) 976069
29.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4038188
98.5%
None 62050
 
1.5%
Emoticons 245
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
610307
15.1%
e 462494
 
11.5%
o 405605
 
10.0%
a 272669
 
6.8%
r 260310
 
6.4%
i 225166
 
5.6%
s 196336
 
4.9%
t 156573
 
3.9%
d 145458
 
3.6%
n 133224
 
3.3%
Other values (85) 1170046
29.0%
None
ValueCountFrequency (%)
ã 18253
29.4%
é 11197
18.0%
á 8923
14.4%
ç 7252
 
11.7%
ó 6189
 
10.0%
ê 1902
 
3.1%
í 1714
 
2.8%
Ó 1543
 
2.5%
õ 908
 
1.5%
ú 886
 
1.4%
Other values (71) 3283
 
5.3%
Emoticons
ValueCountFrequency (%)
😍 74
30.2%
😉 22
 
9.0%
😘 20
 
8.2%
😆 19
 
7.8%
😡 18
 
7.3%
😁 13
 
5.3%
😊 12
 
4.9%
😀 8
 
3.3%
😩 7
 
2.9%
😃 6
 
2.4%
Other values (19) 46
18.8%

Interactions

2024-04-25T07:54:52.012758image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:41.501969image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.270181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.142533image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.929191image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.709479image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.523586image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.407390image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.164201image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.955179image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.763684image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.659717image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.481593image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.229827image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:52.066498image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:41.555404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.324693image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.197822image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.984469image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.765439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.580015image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.459460image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.218226image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.012049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.819440image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.717364image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.535184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.283987image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:52.118986image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:41.608123image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.375844image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.252587image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.037876image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.821710image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.634052image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.511931image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.270988image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.069506image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.873576image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.773310image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.586617image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.340788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:52.173285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:41.665006image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.433628image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.309465image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.095061image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.880062image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.691014image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.567609image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.328657image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.128359image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.931221image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.831104image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.642297image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.398759image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:52.225791image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:41.717835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.487069image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.363929image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.146932image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.935559image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.744483image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.620682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.382542image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.184984image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.984973image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.887439image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.693036image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.454854image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:52.282774image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:41.775985image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.639687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.422251image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.207016image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.999869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.803506image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.676679image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.441753image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.246130image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.045178image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.948115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.750696image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.520419image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:52.339994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:41.831855image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.694106image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.479784image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.262995image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.064919image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.858800image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.732525image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.501313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.306178image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.101935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.016411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.804946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.578681image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:52.390189image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:41.883908image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.744635image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.531963image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.316147image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.121563image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.017802image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.783335image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.558848image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.359708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.155455image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.073746image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.855294image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.630165image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:52.444671image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:41.938807image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.799582image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.590211image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.370921image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.179834image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.072402image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.842064image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.617087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.417913image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.211658image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.133430image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.908218image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.685705image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:52.502332image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:41.997575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.857351image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.650359image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.429609image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.242586image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.130805image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.898961image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.679507image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.476967image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.381070image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.196049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.964933image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.743800image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:52.559765image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.053688image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.920471image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.707827image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.486016image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.301634image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.189239image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.954477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.737009image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.537008image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.437679image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.255057image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.019346image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.801187image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:52.617009image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.112977image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.978837image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.767779image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.544959image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.362082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.247856image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.010064image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.794950image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.598992image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.497366image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.316112image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.076653image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.858808image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:52.776658image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.163761image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.035022image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.819559image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.597552image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.414165image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.300372image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.061028image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.847916image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.652314image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.551521image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.369710image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.125476image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.909708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:52.827088image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:42.217402image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.089189image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:43.874791image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:44.653854image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:45.469939image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:46.353375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.112072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:47.901669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:48.708402image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:49.605162image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:50.426104image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.176904image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-04-25T07:54:51.960129image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2024-04-25T07:54:58.600009image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
payment_sequentialpayment_installmentspayment_valuecustomer_zip_code_prefixorder_item_idpricefreight_valueproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmorder_statuspayment_typecustomer_statereview_score
payment_sequential1.000-0.177-0.214-0.007-0.007-0.0050.016-0.004-0.013-0.0040.0280.0320.0110.0260.0290.2290.0260.012
payment_installments-0.1771.0000.3960.0690.0600.3170.1900.0160.033-0.0030.1980.1090.1060.1250.0050.2730.0320.028
payment_value-0.2140.3961.0000.1060.2570.7900.4240.0240.169-0.0120.4520.2310.3060.2340.0160.0210.0270.028
customer_zip_code_prefix-0.0070.0690.1061.000-0.0090.0700.4660.0160.0310.0260.0260.0090.019-0.0020.0230.0330.8960.042
order_item_id-0.0070.0600.257-0.0091.000-0.114-0.056-0.022-0.032-0.0640.0010.0080.019-0.0030.0030.0210.0030.041
price-0.0050.3170.7900.070-0.1141.0000.4350.0420.2110.0280.5150.2680.3280.2730.0150.0140.0200.012
freight_value0.0160.1900.4240.466-0.0560.4351.0000.0330.1170.0100.4480.2820.2840.2730.0160.0100.0850.015
product_name_lenght-0.0040.0160.0240.016-0.0220.0420.0331.0000.0730.1630.0760.060-0.0570.0660.0190.0110.0130.013
product_description_lenght-0.0130.0330.1690.031-0.0320.2110.1170.0731.0000.1110.095-0.0200.134-0.0810.0170.0200.0270.015
product_photos_qty-0.004-0.003-0.0120.026-0.0640.0280.0100.1630.1111.0000.0030.005-0.080-0.0150.0130.0040.0140.016
product_weight_g0.0280.1980.4520.0260.0010.5150.4480.0760.0950.0031.0000.6180.5300.6190.0120.0170.0280.021
product_length_cm0.0320.1090.2310.0090.0080.2680.2820.060-0.0200.0050.6181.0000.2440.6310.0150.0210.0170.018
product_height_cm0.0110.1060.3060.0190.0190.3280.284-0.0570.134-0.0800.5300.2441.0000.3340.0160.0150.0200.018
product_width_cm0.0260.1250.234-0.002-0.0030.2730.2730.066-0.081-0.0150.6190.6310.3341.0000.0050.0190.0170.013
order_status0.0290.0050.0160.0230.0030.0150.0160.0190.0170.0130.0120.0150.0160.0051.0000.0070.0270.131
payment_type0.2290.2730.0210.0330.0210.0140.0100.0110.0200.0040.0170.0210.0150.0190.0071.0000.0390.009
customer_state0.0260.0320.0270.8960.0030.0200.0850.0130.0270.0140.0280.0170.0200.0170.0270.0391.0000.049
review_score0.0120.0280.0280.0420.0410.0120.0150.0130.0150.0160.0210.0180.0180.0130.1310.0090.0491.000

Missing values

2024-04-25T07:54:53.006364image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-25T07:54:53.351985image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

order_idcustomer_idorder_statuspayment_sequentialpayment_typepayment_installmentspayment_valuecustomer_unique_idcustomer_zip_code_prefixcustomer_citycustomer_stateorder_item_idproduct_idseller_idshipping_limit_datepricefreight_valueproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmproduct_category_name_englishreview_scorereview_comment_message
0e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered1credit_card118.127c396fd4830fd04220f754e42b4e5bff3149sao pauloSP187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.72utilidades_domesticas40.0268.04.0500.019.08.013.0housewares4Não testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
1e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered3voucher12.007c396fd4830fd04220f754e42b4e5bff3149sao pauloSP187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.72utilidades_domesticas40.0268.04.0500.019.08.013.0housewares4Não testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
2e481f51cbdc54678b7cc49136f2d6af79ef432eb6251297304e76186b10a928ddelivered2voucher118.597c396fd4830fd04220f754e42b4e5bff3149sao pauloSP187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-06 11:07:1529.998.72utilidades_domesticas40.0268.04.0500.019.08.013.0housewares4Não testei o produto ainda, mas ele veio correto e em boas condições. Apenas a caixa que veio bem amassada e danificada, o que ficará chato, pois se trata de um presente.
3128e10d95713541c87cd1a2e48201934a20e8105f23924cd00833fd87daa0831delivered1credit_card337.773a51803cc0d012c3b5dc8b7528cb05f73366sao pauloSP187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-08-21 20:05:1629.997.78utilidades_domesticas40.0268.04.0500.019.08.013.0housewares4Deveriam embalar melhor o produto. A caixa veio toda amassada e vou dar de presente.
40e7e841ddf8f8f2de2bad69267ecfbcf26c7ac168e1433912a51b924fbd34d34delivered1credit_card137.77ef0996a1a279c26e7ecbd737be23d2352290sao pauloSP187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-08-08 18:37:3129.997.78utilidades_domesticas40.0268.04.0500.019.08.013.0housewares5Só achei ela pequena pra seis xícaras ,mais é um bom produto\r\n
5bfc39df4f36c3693ff3b63fcbea9e90a53904ddbea91e1e92b2b3f1d09a7af86delivered1boleto144.09e781fdcc107d13d865fc7698711cc57288032florianopolisSC187285b34884572647811a353c7ac498a3504c0cb71d7fa48d967e0e4c94d59d92017-10-31 02:14:1129.9914.10utilidades_domesticas40.0268.04.0500.019.08.013.0housewares3No reviews
66ea2f835b4556291ffdc53fa0b3b95e8c7340080e394356141681bd4c9b8fe31delivered1credit_card10356.123e4fd73f1e86b135b9b121d6abbe959719400presidente venceslauSP1be021417a6acb56b9b50d3fd2714baa8f5f46307a4d15880ca14fab4ad9dfc9b2017-11-30 00:21:09339.0017.12utilidades_domesticas48.0664.06.014300.038.034.034.0housewares1Inicialmente, na data da compra o produto era para ser entregue até 21/12. Hoje é dia 27/12 e não chegou ainda.
782bce245b1c9148f8d19a55b9ff70644388025bec8128ff20ec1a316ed4dcf02delivered1boleto1267.80f9effeed3df9ae063a58c0759b96f8b285804cascavelPR1a5a0e71a81ae65aa335e71c06261e260c8417879a15366a17c30af34c798c3322017-04-27 05:15:5638.0015.56utilidades_domesticas57.0698.03.0705.034.022.028.0housewares1No reviews
882bce245b1c9148f8d19a55b9ff70644388025bec8128ff20ec1a316ed4dcf02delivered1boleto1267.80f9effeed3df9ae063a58c0759b96f8b285804cascavelPR2a5a0e71a81ae65aa335e71c06261e260c8417879a15366a17c30af34c798c3322017-04-27 05:15:5638.0015.56utilidades_domesticas57.0698.03.0705.034.022.028.0housewares1No reviews
982bce245b1c9148f8d19a55b9ff70644388025bec8128ff20ec1a316ed4dcf02delivered1boleto1267.80f9effeed3df9ae063a58c0759b96f8b285804cascavelPR3a5a0e71a81ae65aa335e71c06261e260c8417879a15366a17c30af34c798c3322017-04-27 05:15:5638.0015.56utilidades_domesticas57.0698.03.0705.034.022.028.0housewares1No reviews
order_idcustomer_idorder_statuspayment_sequentialpayment_typepayment_installmentspayment_valuecustomer_unique_idcustomer_zip_code_prefixcustomer_citycustomer_stateorder_item_idproduct_idseller_idshipping_limit_datepricefreight_valueproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmproduct_category_name_englishreview_scorereview_comment_message
1155999980064d9817bacc9e261e7f5fcf3fe5e30e0257e1c7189e6f6ba4ef151e717cdelivered1credit_card7145.5656c8638e7c058b98aae6d74d2dd6ea2337800guaxupeMG1ec90895370885d025efeb8e72e7fa699e7f3bf1ecd8526eb7f3b18059e6716e52017-07-18 07:03:41129.9015.66artes_e_artesanato57.0283.01.01200.020.020.016.0arts_and_craftmanship5No reviews
1156004ec2eea90506ad5dd1284a1a7e4cafeeedb3ca904a6cd3caeecda1f844ebae65delivered1credit_card141.130f8dc6a031c49afe5079f92f04c798c34317sao pauloSP1c51fd9478d188180d962c7ecc35b42d0e819bcfade7b5d88a27325eb6cfd62c52018-08-01 10:45:1529.9011.23artes_e_artesanato44.0447.04.02900.050.010.040.0arts_and_craftmanship5Muito bonito, chegou no prazo. Poderiam ter indicado o número do pedido na embalagem para saber!
11560131a2886d02ad2127bfec204da2e93b6628738edfba72391be47d697e6e7e5770delivered1credit_card127.20e839774c9d31569dba47946296e58b3311035santosSP13a96bcbf644a5d39010757062856802670126eecc6aa1274392a1743866e96782018-07-31 10:35:1719.807.40artes_e_artesanato53.0529.02.0325.027.010.014.0arts_and_craftmanship5Chegou muito, muito antes do prazo
1156021c3f752b9a4d09bf1e016439a427d5f87fe022a904d647429c6e08a8f34c9b22delivered1debit_card123.88931a4a1a3e2cf8b4b4d33922f1469dbe12243sao jose dos camposSP156a1efb30499b4dffd45c8d655199b1155f7a3319d80f7fdf078b8f03e6725fe2018-08-20 23:35:1416.497.39artes_e_artesanato60.0778.02.0100.022.02.015.0arts_and_craftmanship5No reviews
1156032cfc3681e512e0d12b6fe84b396e39076c60b00bcbef919c1697d18c309a99fedelivered1credit_card134.224637328fb3cc446c747c7f83f80c651d38401uberlandiaMG11cf657de01c2b973b898ad0ac9073ac7fd386aa7bed2af3c7035c65506c9b4a32018-04-26 21:31:0515.9918.23artes_e_artesanato41.0130.01.0210.025.010.015.0arts_and_craftmanship4No reviews
1156040b82d0616f1ad8da15cf967b984b4004986632b40c38f4240caf8608cb01d40ddelivered1boleto133.69c887bc0b0717ee4a21d7d22137f12ee330575belo horizonteMG14a24717893a6c8f3cfcf9843b8987d152cb6eb1b7185064167657fa09f5411052018-08-07 04:25:1425.008.69artes_e_artesanato36.0576.05.0350.032.02.028.0arts_and_craftmanship5No reviews
1156052ef4a11b6e24fdfbb43b92cb5f95edffee1cfdc92e449920e25d3ca4ab4da4f6delivered1credit_card184.638d80e477643ec569b24592ce3d9dfc4c9951diademaSP19c313adb4b38a55b092f53f83f78be9eef728fa1f17436c91ed1ccd03dcf96312018-07-30 09:17:3919.0012.86artes_e_artesanato32.0660.03.0500.016.06.016.0arts_and_craftmanship3Um dos frisadores que comprei, especificamente a FOLHA NAO FRISA! NAO REALIZA SUA FUNÇAO. Nao molda o eva, Testei com varios materiais diferentes e infelizmente nao funciona.
1156062ef4a11b6e24fdfbb43b92cb5f95edffee1cfdc92e449920e25d3ca4ab4da4f6delivered1credit_card184.638d80e477643ec569b24592ce3d9dfc4c9951diademaSP2eacb104882d39ffb53140b1d1860a7c3ef728fa1f17436c91ed1ccd03dcf96312018-07-30 09:17:3939.9012.87artes_e_artesanato57.0942.05.0500.021.07.014.0arts_and_craftmanship3Um dos frisadores que comprei, especificamente a FOLHA NAO FRISA! NAO REALIZA SUA FUNÇAO. Nao molda o eva, Testei com varios materiais diferentes e infelizmente nao funciona.
1156072c4ada2e75c2ad41dd93cebb5df5f023363d3a9b2ec5c5426608688ca033292ddelivered1credit_card1209.06d8bee9ec375c3a0f9ef8ed7456a51dcd76940rolim de mouraRO16c7a0a349ad11817745e3ad58abd5c7948162d548f5b1b11b9d29d1e01f75a612017-01-30 11:09:00183.2925.77seguros_e_servicos55.0506.01.01225.027.035.015.0security_and_services4Envio muito rápido. Recomendo.
115608bede3503afed051733eeb4a84d1adcc5919570a26efbd068d6a0f66d5c5072a3delivered1boleto1115.45141e824b8e0df709e3fcf6d982225a8e71940brasiliaDF18db75af9aed3315374db44d7860e25da4e922959ae960d389249c378d1c939f52017-09-26 04:05:52100.0015.45seguros_e_servicos48.0461.04.0400.026.022.011.0security_and_services1boa tarde produto enviado errado, terei que me deslocar aos correios pois moro em um condomínio tive receber, e ainda irei esperar chegar para so depois eu receber o produto correto, paguei via boleto

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